Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin

Machine Learning Algorithms for Robotic Navigation and Perception and Embedded Implementation Techniques

L'abstract è presente nell'allegato / the abstract is in the attachmen

Probabilistic latent variable models for knowledge discovery and optimization

I conduct a systematic study of probabilistic latent variable models (PLVMs) with applications to knowledge discovery and optimization. Probabilistic modeling is a principled means to gain insight of data. By assuming that the observed data are generated from a distribution, we can estimate its density, or the statistics of our interest, by either Maximum Likelihood Estimation or Bayesian inference, depending on whether there is a prior distribution for the parameters of the assumed data distribution. One of the primary goals of various machine learning/data mining models is to reveal the underlying knowledge of observed data. A common practice is to introduce latent variables, which are modeled together with the observations. Such latent variables compute, for example, the class assignments (labels), the cluster membership, as well as other unobserved measurements of the data. Besides, proper exploitation of latent variables facilities the optimization itself, which leads to computationally efficient inference algorithms. In this thesis, I describe a range of applications where latent variables can be leveraged for knowledge discovery and efficient optimization. Works in this thesis demonstrate that PLVMs are a powerful tool for modeling incomplete observations. Through incorporating latent variables and assuming that the observations such as citations, pairwise preferences as well as text are generated following tractable distributions parametrized by the latent variables, PLVMs are flexible and effective to discover knowledge in data mining problems, where the knowledge is mathematically modelled as continuous or discrete values, distributions or uncertainty. In addition, I also explore PLVMs for deriving efficient algorithms. It has been shown that latent variables can be employed as a means for model reduction and facilitates the computation/sampling of intractable distributions. Our results lead to algorithms which take advantage of latent variables in probabilistic models. We conduct experiments against state-of-the-art models and empirical evaluation shows that our proposed approaches improve both learning performance and computational efficiency

The criminal subject : Alphonse Bertillon and Francis Galton, their aesthetics and their legacies

This thesis applies aesthetic language to a variety of practices associated with the production and analysis of criminal identification portraits. Much of what might seem to be standardised in this model of portraiture was influenced by abstract visual techniques that were developed in the late nineteenth century, specifically in the work of Alphonse Bertillon and Francis Galton, which frequently moves away from the judicial, into the experimental. Structured theoretically as opposed to chronologically, this thesis provides a thorough examination of the components - material, technological, temporal, and symbolic - that constitute the identification portrait. The theoretical resonance of Galton’s composite portrait photography and other abstract techniques is seen to inform twentieth century and recent debates on photographic portraiture, and the transformation of the portrait for which Bertillon was responsible, which placed great emphasis on the need to summarise, even memorise, a subject’s ‘data’ for police purposes, is found to have a legacy that extends far beyond the standardised ‘mug shot’ into much more imaginary territories. Jacques Derrida’s terminology for the supplement, Roland Barthes’ commentaries on the photographic portrait, Julia Kristeva’s model of colour perception, and Gilles Deleuze and Félix Guattari’s notion of the ‘body without organs’, are some of the many theoretical models with which this material is seen to resonate